Comparative analysis of solving traveling salesman problem using artificial intelligence algorithms

2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) Pub Date : 2017-12-01 DOI:10.1109/HNICEM.2017.8269423

S. G. Brucal, E. Dadios

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引用次数: 13

Abstract

This paper aims to provide a comparative study of the different artificial intelligence (AI) algorithms applied to solve the traveling salesman problem (TSP). Four (4) AI algorithms such as genetic algorithm, nearest neighbor, ant colony optimization, and neuro-fuzzy are executed in MatLab software to determine which among these techniques will provide the least execution time to solve a TSP. The objective of comparing and analyzing each AI algorithm — as applied to a single problem with the different program execution — is to identify if significant difference in execution time could lead to significant saving in energy consumption. The simulations using MatLab resulted to strong correlation at an R2 of 0.95 in the average execution time with the number of code lines, but do not give a significant execution time variance as when ANOVA and t-test measures were performed. The result of this paper could be used as a basis in the design phase of software development life cycle to arrive into an energy efficient software application with respect to time needed to execute a program.

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用人工智能算法求解旅行商问题的比较分析

本文旨在对不同的人工智能(AI)算法在求解旅行商问题(TSP)中的应用进行比较研究。在MatLab软件中执行遗传算法、最近邻算法、蚁群算法和神经模糊算法等四种人工智能算法，以确定哪种技术在求解TSP时执行时间最短。比较和分析每个人工智能算法的目的——应用于不同程序执行的单个问题——是为了确定执行时间的显著差异是否会导致能耗的显著节省。使用MatLab进行的模拟结果显示，平均执行时间与代码行数的R2为0.95，具有很强的相关性，但执行ANOVA和t检验措施时，执行时间方差并不显著。本文的结果可以作为软件开发生命周期的设计阶段的基础，从而在执行程序所需的时间方面达到节能的软件应用程序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

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